robotics
The Generalist Took the Robot Controls
A single agent harness ran unchanged across single-arm, bimanual and mobile manipulation, outperforming specialist policies in the authors’ tests.
Summary
A single agent harness ran unchanged across single-arm, bimanual and mobile manipulation, outperforming specialist policies in the authors’ tests.
AMP1 lets a general-purpose AI model command a manipulator directly and revise actions as a task unfolds, without demonstrations or task-specific training. The authors report 85.6 percent success on RoboLab-120, versus 49.2 and 42.9 percent for compared vision-language-action and world-action policies, plus 104 successes in 108 real-world trials across twelve tasks and three robot instances. A persistent memory reduced failure on a separate bimanual benchmark by 4.6 times relative to zero-shot deployment. Those results come from the paper’s benchmarks and hardware set; they do not establish general real-world reliability, but they challenge the assumption that manipulation needs a specialist policy for every setting.
Why it matters
A single agent harness ran unchanged across single-arm, bimanual and mobile manipulation, outperforming specialist policies in the authors’ tests.
Limits and context
- Those results come from the paper’s benchmarks and hardware set; they do not establish general real-world reliability, but they challenge the assumption that manipulation needs a specialist policy for every setting.
Key claims
A single agent harness ran unchanged across single-arm, bimanual and mobile manipulation, outperforming specialist policies in the authors’ tests.
Qualification: Those results come from the paper’s benchmarks and hardware set; they do not establish general real-world reliability, but they challenge the assumption that manipulation needs a specialist policy for every setting.
Evidence: source-2026-11-09-003
Sources
- arXiv preprint 2611.09892, original v1arXiv · primary research
Corrections
No corrections have been recorded for this story.